ERJ
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Published online before print March 1, 2007, 10.1183/09031936.00002907
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
29/6/1120    most recent
09031936.00002907v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Permissions
Right arrowRequest Permissions
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (1)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by He, J-Q.
Right arrow Articles by Sandford, A. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by He, J-Q.
Right arrow Articles by Sandford, A. J.
Eur Respir J 2007; 29:1120-1126
Copyright ©ERS Journals Ltd 2007

Polymorphisms of interleukin-10 and its receptor and lung function in COPD

J-Q. He1, K. Shumansky1, X. Zhang1, J. E. Connett2, N. R. Anthonisen3 and A. J. Sandford1

1 The James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research, St. Paul’s Hospital, University of British Columbia, Vancouver, BC, and 3 Faculty of Medicine, University of Manitoba, Winnipeg, MB, Canada. 2 Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.

CORRESPONDENCE: A. J. Sandford, UBC James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research, St. Paul’s Hospital, 1081 Burrard Street Vancouver, BC, V6Z 1Y6, Canada. Fax: 1 6048068351. E-mail: asandford{at}mrl.ubc.ca

Keywords: Chronic obstructive pulmonary disease, forced expiratory volume in one second, genetic polymorphism, interleukin-10, interleukin-10 receptor{alpha}, lung function

Received: January 8, 2007
Accepted February 9, 2007


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 SUMMARY
 ACKNOWLEDGEMENTS
 REFERENCES
 
Interleukin (IL)-10 is a type-2 T-helper cell cytokine with a broad spectrum of anti-inflammatory actions. Inflammation plays an important role in the pathogenesis of chronic obstructive pulmonary disease. It was hypothesised that single nucleotide polymorphisms (SNPs) of the genes encoding IL-10 (IL10) and the {alpha} subunit of its receptor (IL10RA) are associated with changes in, or value of, forced expiratory volume in one second (FEV1) in smoking-induced chronic obstructive pulmonary disease.

In total, eleven SNPs of IL10 and IL10RA were studied in 586 White subjects, selected from continuous smokers followed for 5 yrs in the Lung Health Study, who showed the fastest (n = 280) and slowest (n = 306) decline in FEV1. These 11 SNPs were also studied in 1,072 participants exhibiting the lowest (n = 538) and highest (n = 534) baseline FEV1 at the beginning of the Lung Health Study.

No association was found in the primary analyses. Although a subgroup analysis showed that the IL-10 3368A allele was associated with a fast decline in FEV1, the association did not pass correction for multiple comparisons. No gene–gene interaction of IL10 with IL10RA was found.

There was no association of polymorphisms of the genes encoding interleukin-10 and the {alpha} subunit of its receptor with the rate of decline in, or value of, forced expiratory volume in one second in smoking-induced chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is characterised by airway obstruction that is not fully reversible. Airway obstruction is defined as a ratio of forced expiratory volume in one second (FEV1) to forced vital capacity of <0.70. The FEV1, presented as a percentage of the predicted value for age, height, weight and race, defines disease severity. An accelerated rate of decline in FEV1 is the hallmark of COPD. Accumulating evidence suggests that genetic factors account for 30–50% of the variability in cross-sectional FEV1 1, 2, and 18% of the variability in longitudinal change in FEV1 3. However, the genetic determinants of cross-sectional and longitudinal lung function are not fully understood in smoking-induced COPD.

The interleukin (IL)-10 gene (IL10) is located at 1q31–32, a region not linked with lung function in COPD linkage studies 4–6. IL10 was selected as a candidate gene based on its known function and previous association studies. IL-10 is a T-helper cell (Th) type 2 cytokine that has a broad spectrum of anti-inflammatory actions. IL-10 levels are reduced in induced sputum from patients with COPD, indicating that this might be a mechanism for increasing lung inflammation 7. Several single nucleotide polymorphisms (SNPs) and haplotypes of IL10 have been associated with the level of expression of IL-10 8, 9. The SNPs of IL10 were associated with FEV1 in asthmatic subjects 10. A study showed that one IL10 SNP was associated with rate of decline in lung function in firefighters 11.

The IL-10 receptor (IL-10R) is a heterotetramer composed of two of each of the receptor chains IL-10R{alpha} and IL-10Rß. The IL-10R{alpha} chain plays a dominant role in mediating high-affinity ligand binding and signal transduction, whereas the IL-10Rß subunit is thought to be required for signalling alone. It was reported that two SNPs of the IL-10R{alpha} gene (IL10RA; located at 11q23.3), substitutions of serine 138 with glycine and of glycine 330 with arginine, have functional implications 12. There is an interaction between the IL-10 and IL-10R complex; IL-10 stabilises dimerisation of both IL-10R subunits, and exerts a variety of immunoregulatory effects 13.

It was hypothesised that IL10 and IL10RA SNPs and their interaction influence the decline in, and/or cross-sectional value of, FEV1 in smoking-induced COPD. The Lung Health Study (LHS), sponsored by the National Heart, Lung and Blood Institute (Bethesda, MD, USA), was a clinical trial of the effects of smoking intervention and bronchodilator treatment on the progression of COPD 14. This dataset provides an excellent opportunity for exploring genetic determinants of decline, in and/or cross-sectional level of, lung function in patients who exhibit smoking-induced COPD 15–18.


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 SUMMARY
 ACKNOWLEDGEMENTS
 REFERENCES
 
Study subjects
The LHS recruited a total of 5,887 smokers, aged 35–60 yrs, showing spirometric evidence of mild-to-moderate lung function impairment from 10 North American medical centres 14. From the LHS cohort, two nested case–control studies were designed to study genetic determinants of the rate of decline in FEV1 and cross-sectional FEV1. Based on the change in FEV1 during 5 yrs of follow-up, the 287 and 308 non-Hispanic White subjects showing the highest (fast decline group) and slowest (nondecline group) rate of decline in FEV1 were selected from among 3,216 continuous smokers. From all remaining LHS participants, the 484 and 468 non-Hispanic White subjects showing the highest (high function group) and lowest (low function group) post-bronchodilator FEV1 (percent pred) at the start of the LHS were selected. Since 144 individuals from the rate of decline study groups exhibited baseline FEV1 within one of these categories (58 individuals in the high function group and 86 in the low function group), they were also analysed in the baseline function study. Thus there were 542 and 554 participants in the high and low function groups, respectively. Informed consent was obtained from all participants, and the present investigation received the approval of the Providence Health Care Research Ethics Board (Vancouver, BC, Canada).

Tagging SNP selection
The IL10 and IL10RA SNP discovery data were downloaded from the SeattleSNPs Website 19. The IL-10 discovery data from the Innate Immunity Website 20 were incorporated into data from SeattleSNPs. From all SNPs identified in the 23 unrelated European-American samples from the Human Polymorphism Study Centre (CEPH) family panel, a set of tagging SNPs (tagSNPs) was chosen for each gene using the LDSelect program developed by Carlson et al. 21. A relatively stringent linkage disequilibrium (LD) threshold of r2>0.8 and minor allele frequency of 10% was used. Four SNPs of IL10 (fig. 1Go) and seven of IL10RA were selected using the LDSelect program. The positions of the selected tagSNPs in the genes are shown in figures 2Go and 3Go. The corresponding reference sequence positions are shown in table 1Go.


Figure 1
View larger version (65K):
[in this window]
[in a new window]

  		Fig. 1— Tagging (tag) single nucleotide polymorphism (SNP) selection for the interleukin (IL)-10 gene SNPs using the LDSelect program 21. The IL10 genotype data came from two sources, SeattleSNPs 19 and Innate Immunity 20 programmes for genomic applications. Each row corresponds to an individual DNA sample in the SeattleSNPs and Innate Immunity polymorphism discovery panel; E001–E023 represent European-Americans. Columns correspond to polymorphic sites (numbers correspond to the site of the SNP within the gene). Sites are ordered by linkage disequilibrium, with sites showing similar genotype patterns placed in the same bin. All of the tagSNPs were selected; genotyped SNPs are indicated with vertical arrows.

 

Figure 2
View larger version (4K):
[in this window]
[in a new window]

  		Fig. 2— Position of single nucleotide polymorphisms within the interleukin-10 gene. Numbered regions represent exons. C: cytosine; A: adenine; G: guanine; T: thymidine; UTR: untranslated region.

 

Figure 3
View larger version (8K):
[in this window]
[in a new window]

  		Fig. 3— Position of single nucleotide polymorphisms within the interleukin-10 receptor {alpha} gene. Numbered regions represent exons. G: guanine; A: adenine; Ala: alanine; Ser: serine; Gly: glycine; Arg: arginine; UTR: untranslated region.

 

View this table:
[in this window]
[in a new window]

  		Table 1— Nomenclature of single nucleotide polymorphisms(SNPs) utilised in present study

 
Genotyping
Genotyping was performed in 384-well plates using the TaqMan 5’ exonuclease assay with primers and probes supplied by the manufacturer (Applied Biosystems, Foster City, CA, USA). In each plate, 12 DNA samples from the CEPH panel of known genotype were included as positive controls, and eight wells lacking templates as negative controls. No errors were detected in the 10% of randomly selected samples that were genotyped in duplicate.

Statistical analysis
Hardy–Weinberg equilibrium tests and LD estimation were performed using R 22, as were all single-locus association tests. The codominant and additive models were tested first, and then, if there was a significant association, the dominant and recessive models were tested to see whether these models fitted better. If the cell counts were low, significance was assessed by permutation tests. The Armitage trend test 23 was used to test for an additive effect of the allele. In the rate of decline study, multivariate logistic regression analysis was used to adjust for confounding factors, such as age, sex, cumulative cigarette consumption and research centre. In the baseline study, multivariate logistic regression was used to adjust for the aforementioned confounding factors and an additional rate of decline of lung function covariate.

Haplotype association was also tested using R 24. Expectation-maximisation was used to calculate haplotype frequencies.

Gene–gene interactions
The multifactor dimensionality reduction (MDR) and focused interaction testing framework (FITF) methods were used to explore gene–gene interactions. The MDR method is a nonparametric and genetic model-free alternative to logistic regression for detecting and characterising nonlinear interactions among factors associated with disease risk 25. The more-recent FITF approach tests the association of marginal effects and interactions using a logistic regression framework in a series of stages, the number of interacting loci considered increasing with each subsequent stage 26.


   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 SUMMARY
 ACKNOWLEDGEMENTS
 REFERENCES
 
Characteristics of the study groups
The characteristics of the study participants are shown in table 2Go. Since no DNA was available for nine subjects in the decline study and 24 in the baseline FEV1 study, the numbers of participants in the two studies were 586 and 1,072, respectively. There were significant differences in several potential confounding factors, such as age, sex and cigarette smoking history (in pack-years) between the fast and nondecline FEV1 groups and also the high and low function (FEV1) groups. Therefore, the associations of alleles, genotypes and haplotypes with lung function were analysed using logistic regression to adjust for these factors.


View this table:
[in this window]
[in a new window]

  		Table 2— Distribution of demographic characteristics in fast/nondecline and high/low function groups

 
The allele frequencies of all 11 SNPs did not significantly deviate from Hardy–Weinberg equilibrium in the nondecline and high function groups.

Single SNP analysis
The minor allele frequencies of the IL10 and IL10RA SNPs in different study groups are shown in table 3Go. Multivariate logistic regression analysis was used to adjust for confounding factors, such as age, sex, cumulative cigarette consumption and research centre, in the rate of decline study, and the aforementioned confounding factors and an additional rate of decline of lung function covariate in the baseline study. There was no association of any of the 11 SNPs studied with rate of decline or level of FEV1 in additive and codominant genetic models before or after adjustment for confounding factors (p>0.05 for all).


View this table:
[in this window]
[in a new window]

  		Table 3— Minor allele frequencies of single nucleotide polymorphisms(SNPs) in the study groups

 
In a subgroup analysis stratified by sex, a significant association of the IL10 3368G/A SNP with rate of decline in FEV1 was found in female participants; the frequencies of the 3368A allele were 14.9 and 22.6% in nondecliners and fast decliners, respectively (p = 0.02 for the additive model; odds ratio 1.92 (95% confidence interval 1.06–3.48). However, this association did not survive correction for multiple comparisons.

Haplotype analysis
Logistic regression analysis was performed to estimate haplotype disease association. The haplotype frequencies inferred by the expectation-maximisation algorithm are presented in table 4Go. In the rate of decline study, the four IL10 and seven IL10RA SNP haplotypes were not associated with lung function decline (p>0.05 for all). In the baseline lung function study, the frequency of the IL10RA haplotype 5850A/6865A/6881G/9826A/1371G/12488A/14904G (AAGAGAG) was significantly lower in the high versus the low function group (14.1 versus 15.0%) compared with the GGAGGGA haplotype as a reference (adjusted p = 0.02). The frequency of the IL10RA haplotype GGAGGGG was marginally lower in the high versus the low function group (7.2 versus 8.9%) compared with the GGAGGGA haplotype as a reference (adjusted p = 0.05). However, there were no significant differences in global haplotype tests of association.


View this table:
[in this window]
[in a new window]

  		Table 4— Haplotype frequencies inferred using the expectation-maximisation algorithm in non-/fast decline and high/low function participants

 
Gene–gene interaction
The IL10 and IL10RA interaction of all possible two-to-four-locus models was explored using the MDR method. There was no evidence of epistasis (gene–gene interaction; detailed results not shown). The result of no epistasis was further confirmed using the FITF strategy, which was suggested to be more powerful than MDR (detailed results not shown) 26.


   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
SUMMARY
 ACKNOWLEDGEMENTS
 REFERENCES
 
Association studies of the IL10 and IL10RA SNPs with rate of decline and baseline FEV1 were performed using two nested case–control studies in the LHS. No association was found with either the rate of decline in, or level of, lung function in smoking-induced COPD subjects.

Several lines of evidence drove the analysis of the present data to be stratified by sex. First, it was reported that females might be more susceptible to the development of severe COPD 27. Secondly, there was a lower Th1/Th2 cytokine production ratio by stimulated T-cells in females compared with males, which was due to females exhibiting lower levels of Th1 cytokines (IL-2 and interferon-{gamma}) and higher levels of Th2 cytokines (IL-10 and -4) after stimulation 28. Thirdly, several studies have demonstrated that associations of IL10 SNPs with IL-10 expression levels and diseases are sex-specific 29–31. Although a subgroup analysis stratified by sex showed that the IL10 3368A allele was associated with a fast decline in FEV1 (p = 0.02 for the additive model), the association disappeared after adjustment for multiple comparisons.

There have been three previous studies of IL10 SNPs and lung function or COPD (table 5Go) 10, 11, 32. In order to compare the present results with those of previous studies, LD information must be established for all studied SNPs. The SeattleSNPs and Innate Immunity SNP discovery data in European-Americans (fig. 1Go) are shown in table 5Go. IL10 734G/T was in perfect LD with IL10 -1117A/G (IL10 1082 in 10) and IL10 3916T/C (IL10 4299 in 10). Similarly, IL10 -627C/A was in almost perfect LD with IL10 -854C/T (R2 = 0.94) 10, and IL10 3368G/A was in perfect LD with IL10 1812G/T (fig. 1Go).


View this table:
[in this window]
[in a new window]

  		Table 5— Association studies of interleukin(IL)-10 gene single nucleotide polymorphisms (SNPs) with rate of decline in or level of lung function

 
A comparison of the present data and previously reported results is presented in table 5Go. Although all three previous studies concluded that IL10 SNPs were associated with FEV1 or FEV1 change, either the study subjects were not COPD patients or the associations would disappear if adjusted for multiple comparisons. In the report of Burgess et al. 11, the rate of FEV1 decline was studied in 379 firefighters with respect to five IL10 SNPs, and only a single SNP (1668AA homozygote) was found to be associated with FEV1 decline (p = 0.023 before adjusting for multiple comparisons). In the paper of Seifart et al. 32, five SNPs were studied in three groups (COPD patients, population controls and matched controls), and only moderate evidence of association with COPD was found for IL10-1082A/G compared with population controls before adjusting for multiple comparisons (p = 0.0247 for genotype distribution; p = 0.047 for allele frequency). In addition, the associations of these two studies were not found for the same SNP or SNPs in the same LD bin. Therefore, the positive associations in these two studies may represent a type I error.

In the study of Lyon et al. 10, which included 518 Caucasian asthmatic children from the Childhood Asthma Management Program (CAMP) and their parents, IL10 4299T/C was significantly associated with FEV1 (percent pred) in both the family and population analyses (p = 0.0002), which were still significant even after adjusting for multiple comparisons.

There are several explanations for the inconsistency of the present results compared with those of Lyon et al. 10. The first and most important concern is the issue of the power of the study when a negative result is found. IL10 4299T/C was in perfect LD with IL10 734G/T, as mentioned above, and the IL10 4299T allele frequency was 48.5% in the CAMP study, which is similar to the IL10 734T allele frequency in the present study (48.7–52.9% in four different phenotypic groups). The results of previous power analysis in the present study samples showed that, for an allele frequency of 48.7% and the genetic models studied, there was a >90% power for detecting an allele/genotype relative risk in the region of ≥1.5 in the baseline lung function groups 33. Therefore, it is unlikely that the present negative association was due to lack of power. Secondly, the subjects in the CAMP study were asthmatic children, whereas the present study subjects were adults with smoking-induced COPD. The factors that determine baseline lung function may be different in these two different disease entities and/or age groups.

The effects of interaction of IL10 and IL10RA SNPs on the rate of decline and baseline FEV1 were explored using both MDR and FITF methods, but there was no evidence of gene–gene interaction. Since relatively stringent criteria were used to select tagSNPs and powerful methodologies were used to perform the analysis, this observation is probably correct.


   SUMMARY
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 SUMMARY
ACKNOWLEDGEMENTS
 REFERENCES
 
There was no association of single nucleotide polymorphisms of the genes encoding interleukin-10 and the {alpha} subunit of its receptor with rate of decline in, or level of, forced expiratory volume in one second in patients with mild-to-moderate smoking-induced chronic obstructive pulmonary disease.


   ACKNOWLEDGEMENTS
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 SUMMARY
 ACKNOWLEDGEMENTS
REFERENCES
 
The authors gratefully acknowledge the National Heart, Lung, and Blood Institute for the recruitment and characterisation of the present study.


   REFERENCES
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 SUMMARY
 ACKNOWLEDGEMENTS
 REFERENCES
 

  1. Lewitter FI, Tager IB, McGue M, Tishler PV, Speizer FE. Genetic and environmental determinants of level of pulmonary function. Am J Epidemiol 1984;120:518–530.[Abstract/Free Full Text]
  2. Wilk JB, Djousse L, Arnett DK, et al. Evidence for major genes influencing pulmonary function in the NHLBI family heart study. Genet Epidemiol 2000;19:81–94.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  3. Gottlieb DJ, Wilk JB, Harmon M, et al. Heritability of longitudinal change in lung function. The Framingham study. Am J Respir Crit Care Med 2001;164:1655–1659.[Abstract/Free Full Text]
  4. Joost O, Wilk JB, Cupples LA, et al. Genetic loci influencing lung function: a genome-wide scan in the Framingham Study. Am J Respir Crit Care Med 2002;165:795–799.[Abstract/Free Full Text]
  5. Wilk JB, DeStefano AL, Arnett DK, et al. A genome-wide scan of pulmonary function measures in the National Heart, Lung, and Blood Institute Family Heart Study. Am J Respir Crit Care Med 2003;167:1528–1533.[Abstract/Free Full Text]
  6. Silverman EK, Palmer LJ, Mosley JD, et al. Genomewide linkage analysis of quantitative spirometric phenotypes in severe early-onset chronic obstructive pulmonary disease. Am J Hum Genet 2002;70:1229–1239.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  7. Takanashi S, Hasegawa Y, Kanehira Y, et al. Interleukin-10 level in sputum is reduced in bronchial asthma, COPD and in smokers. Eur Respir J 1999;14:309–314.[Abstract]
  8. Kurreeman FA, Schonkeren JJ, Heijmans BT, Toes RE, Huizinga TW. Transcription of the IL10 gene reveals allele-specific regulation at the mRNA level. Hum Mol Genet 2004;13:1755–1762.[Abstract/Free Full Text]
  9. Turner DM, Williams DM, Sankaran D, Lazarus M, Sinnott PJ, Hutchinson IV. An investigation of polymorphism in the interleukin-10 gene promoter. Eur J Immunogenet 1997;24:1–8.[Web of Science][Medline] [Order article via Infotrieve]
  10. Lyon H, Lange C, Lake S, et al. IL10 gene polymorphisms are associated with asthma phenotypes in children. Genet Epidemiol 2004;26:155–165.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  11. Burgess JL, Fierro MA, Lantz RC, et al. Longitudinal decline in lung function: evaluation of interleukin-10 genetic polymorphisms in firefighters. J Occup Environ Med 2004;46:1013–1022.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  12. Gasche C, Grundtner P, Zwirn P, et al. Novel variants of the IL-10 receptor 1 affect inhibition of monocyte TNF-{alpha} production. J Immunol 2003;170:5578–5582.[Abstract/Free Full Text]
  13. Moore KW, de Waal Malefyt R, Coffman RL, O’Garra A. Interleukin-10 and the interleukin-10 receptor. Annu Rev Immunol 2001;19:683–765.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  14. Anthonisen NR, Connett JE, Kiley JP, et al. Effects of smoking intervention and the use of an inhaled anticholinergic bronchodilator on the rate of decline of FEV1. The Lung Health Study. JAMA 1994;272:1497–1505.[Abstract/Free Full Text]
  15. Sandford AJ, Chagani T, Weir TD, Connett JE, Anthonisen NR, Paré PD. Susceptibility genes for rapid decline of lung function in the Lung Health Study. Am J Respir Crit Care Med 2001;163:469–473.[Abstract/Free Full Text]
  16. He JQ, Connett J, Anthonisen N, Paré P, Sandford A. The effects of polymorphisms of GSTM1, T1 and P1 gene or their interaction with cigarette exposure on lung function. Am J Respir Crit Care Med 2003;167:A744
  17. He JQ, Connett JE, Anthonisen NR, Pare PD, Sandford AJ. Glutathione S-transferase variants and their interaction with smoking on lung function. Am J Respir Crit Care Med 2004;170:388–394.[Abstract/Free Full Text]
  18. He JQ, Connett JE, Anthonisen NR, Sandford AJ. Polymorphisms in the IL13, IL13RA1, and IL4RA genes and rate of decline in lung function in smokers. Am J Respir Cell Mol Biol 2003;28:379–385.[Abstract/Free Full Text]
  19. National Heart, Lung and Blood Institute. SeattleSNPs Variation Discovery Resource. http://pga.gs.washington.edu Date last accessed: October 2003
  20. Channing Laboratory. Innate Immunity in Heart, Lung and Blood Disease. Programs for Genomic Applications. http://innateimmunity.net/ Date last accessed October 2003
  21. Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am J Hum Genet 2004;74:106–120.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  22. Hornik K. Frequently Asked Questions on R. R: a Language and Environment for Statistical Computing. Vienna, 2004. http://www.r-project.org/
  23. Armitage P. Tests for linear trends in proportions and frequencies. Biometrics 1955;11:375–386.[Medline] [Order article via Infotrieve]
  24. Burkett K, McNeney B, Graham J. A note on inference of trait associations with SNP haplotypes and other attributes in generalized linear models. Hum Hered 2004;57:200–206.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  25. Hahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction software for detecting gene–gene and gene–environment interactions. Bioinformatics 2003;19:376–382.[Abstract/Free Full Text]
  26. Millstein J, Conti DV, Gilliland FD, Gauderman WJ. A testing framework for identifying susceptibility genes in the presence of epistasis. Am J Hum Genet 2006;78:15–27.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  27. Silverman EK, Weiss ST, Drazen JM, et al. Gender-related differences in severe, early-onset chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2000;162:2152–2158.[Abstract/Free Full Text]
  28. Giron-Gonzalez JA, Moral FJ, Elvira J, et al. Consistent production of a higher TH1:TH2 cytokine ratio by stimulated T cells in men compared with women. Eur J Endocrinol 2000;143:31–36.[Abstract]
  29. Reuss E, Fimmers R, Kruger A, Becker C, Rittner C, Hohler T. Differential regulation of interleukin-10 production by genetic and environmental factors – a twin study. Genes Immun 2002;3:407–413.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  30. Lio D, Scola L, Crivello A, et al. Gender-specific association between -1082 IL-10 promoter polymorphism and longevity. Genes Immun 2002;3:30–33.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  31. Padyukov L, Hytonen AM, Smolnikova M, et al. Polymorphism in promoter region of IL10 gene is associated with rheumatoid arthritis in women. J Rheumatol 2004;31:422–425.[Abstract/Free Full Text]
  32. Seifart C, Dempfle A, Plagens A, et al. TNF-{alpha}-, TNF-ß-, IL-6-, and IL-10-promoter polymorphisms in patients with chronic obstructive pulmonary disease. Tissue Antigens 2005;65:93–100.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  33. Tanaka G, Sandford AJ, Burkett K, et al. Tumour necrosis factor and lymphotoxin A polymorphisms and lung function in smokers. Eur Respir J 2007;29:34–41.[Abstract/Free Full Text]



This article has been cited by other articles:


Home page
 ThoraxHome page
J-Q He, M G Foreman, K Shumansky, X Zhang, L Akhabir, D D Sin, S F P Man, D L DeMeo, A A Litonjua, E K Silverman, et al.
Associations of IL6 polymorphisms with lung function decline and COPD
Thorax, August 1, 2009; 64(8): 698 - 704.
[Abstract] [Full Text] [PDF]

Home page
 Eur Respir JHome page
J-Q. He, K. Shumansky, J. E. Connett, N. R. Anthonisen, P. D. Pare, and A. J. Sandford
Association of genetic variations in the CSF2 and CSF3 genes with lung function in smoking-induced COPD
Eur. Respir. J., July 1, 2008; 32(1): 25 - 34.
[Abstract] [Full Text] [PDF]

Home page
 J. Clin. Pathol.Home page
J R Kerr, B Burke, R Petty, J Gough, D Fear, D L Mattey, J S Axford, A G Dalgleish, and D J Nutt
Seven genomic subtypes of chronic fatigue syndrome/myalgic encephalomyelitis: a detailed analysis of gene networks and clinical phenotypes
J. Clin. Pathol., June 1, 2008; 61(6): 730 - 739.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Respir. Crit. Care Med.Home page
W. MacNee
Update in Chronic Obstructive Pulmonary Disease 2007
Am. J. Respir. Crit. Care Med., April 15, 2008; 177(8): 820 - 829.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
29/6/1120    most recent
09031936.00002907v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Permissions
Right arrowRequest Permissions
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (1)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by He, J-Q.
Right arrow Articles by Sandford, A. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by He, J-Q.
Right arrow Articles by Sandford, A. J.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS